

"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowercase   :	Any												=						logging.get_logger(__name__)





def       lowercase__						(       snake_case_   :Union[tf.Tensor, np.ndarray]    ):
	if isinstance(snake_case_			,		np.ndarray    ):
		return list(tensor.shape    )

	__UpperCAmelCase      		= tf.shape(snake_case_    )

	if tensor.shape == tf.TensorShape(snake_case_    ):
		return dynamic

	__UpperCAmelCase      		= tensor.shape.as_list()

	return [dynamic[i] if s is None else s for i, s in enumerate(snake_case_    )]





def       lowercase__						(       snake_case_   :tf.Tensor			,		snake_case_   :Optional[int] = None			,		snake_case_   :Optional[str] = None    ):
	return tf.nn.softmax(logits=logits + 1E-9			,		axis=snake_case_			,		name=snake_case_    )





def       lowercase__						(       snake_case_   :List[Any]			,		snake_case_   :Any			,		snake_case_   :Optional[Any]			,		snake_case_   :List[Any]=1E-5			,		snake_case_   :Optional[Any]=-1    ):
	# This is a very simplified functional layernorm, designed to duplicate
	# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
	# models in Transformers.

	if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case_			,		snake_case_    ):
		raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''    )

	# Get mean and variance on the axis to be normalized
	__UpperCAmelCase		,							__UpperCAmelCase      		= tf.nn.moments(snake_case_			,		axes=[axis]			,		keepdims=snake_case_    )

	if axis != -1:
		# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
		# on every dimension except axis
		__UpperCAmelCase      		= [1] * inputs.shape.rank
		__UpperCAmelCase      		= shape_list(snake_case_    )[axis]
		__UpperCAmelCase      		= tf.reshape(snake_case_			,		snake_case_    )
		__UpperCAmelCase      		= tf.reshape(snake_case_			,		snake_case_    )

	# Compute layer normalization using the batch_normalization
	# function.
	__UpperCAmelCase      		= tf.nn.batch_normalization(
	    snake_case_			,		snake_case_			,		snake_case_			,		offset=snake_case_			,		scale=snake_case_			,		variance_epsilon=snake_case_			,		)
	return outputs





def       lowercase__						(       snake_case_   :Any			,		snake_case_   :str=0			,		snake_case_   :Union[str, Any]=-1    ):
	# Replicates the behavior of torch.flatten in TF

	# If end_dim or start_dim is negative, count them from the end
	if end_dim < 0:
		end_dim += input.shape.rank
	if start_dim < 0:
		start_dim += input.shape.rank

	if start_dim == end_dim:
		return input

	__UpperCAmelCase      		= tf.shape(snake_case_    )
	__UpperCAmelCase      		= tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]    )
	__UpperCAmelCase      		= tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,		axis=0    )
	return tf.reshape(snake_case_			,		snake_case_    )





def       lowercase__						(       snake_case_   :tf.Tensor    ):
	if not isinstance(snake_case_			,		tf.Tensor    ):
		__UpperCAmelCase      		= tf.convert_to_tensor(snake_case_    )  # Catches stray NumPy inputs
	if encoder_attention_mask.shape.rank == 3:
		__UpperCAmelCase      		= encoder_attention_mask[:, None, :, :]
	if encoder_attention_mask.shape.rank == 2:
		__UpperCAmelCase      		= encoder_attention_mask[:, None, None, :]
	# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
	# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
	# /transformer/transformer_layers.py#L270
	# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
	# encoder_extended_attention_mask.transpose(-1, -2))
	__UpperCAmelCase      		= (
	    tf.cast(1			,		encoder_attention_mask.dtype    ) - encoder_extended_attention_mask
	) * encoder_extended_attention_mask.dtype.min

	return encoder_extended_attention_mask





def       lowercase__						(       snake_case_   :tf.Tensor			,		snake_case_   :int			,		snake_case_   :str = "input_ids"    ):
	tf.debugging.assert_less(
	    snake_case_			,		tf.cast(snake_case_			,		dtype=tensor.dtype    )			,		message=(
	        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case_    )}) must be smaller than the embedding '''
	        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
	    )			,		)





def       lowercase__						(       snake_case_   :Tuple			,		snake_case_   :int			,		snake_case_   :str    ):
	__UpperCAmelCase      		= 64_512
	# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
	# because in that case even chunking the array would not make the saving
	# possible.
	__UpperCAmelCase      		= [x for x in data if len(snake_case_    ) > HDF5_OBJECT_HEADER_LIMIT]

	# Expecting this to never be true.
	if bad_attributes:
		raise RuntimeError(
		    '''The following attributes cannot be saved to HDF5 file because '''
		    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
		    F'''bytes: {bad_attributes}'''    )

	__UpperCAmelCase      		= np.asarray(snake_case_    )

	__UpperCAmelCase      		= 1
	__UpperCAmelCase      		= np.array_split(snake_case_			,		snake_case_    )

	# This will never loop forever thanks to the test above.
	while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data    ):
		num_chunks += 1
		__UpperCAmelCase      		= np.array_split(snake_case_			,		snake_case_    )

	if num_chunks > 1:
		for chunk_id, chunk_data in enumerate(snake_case_    ):
			__UpperCAmelCase      		= chunk_data
	else:
		__UpperCAmelCase      		= data





def       lowercase__						(       snake_case_   :Optional[int]			,		snake_case_   :Dict    ):
	if name in group.attrs:
		__UpperCAmelCase      		= [n.decode('''utf8'''    ) if hasattr(snake_case_			,		'''decode'''    ) else n for n in group.attrs[name]]
	else:
		__UpperCAmelCase      		= []
		__UpperCAmelCase      		= 0
		while "%s%d" % (name, chunk_id) in group.attrs:
			data.extend(
			    [n.decode('''utf8'''    ) if hasattr(snake_case_			,		'''decode'''    ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]    )
			chunk_id += 1
	return data





def       lowercase__						(       snake_case_   :Dict    ):

	def _expand_single_ad_tensor(snake_case_   :Tuple    ):
		if isinstance(snake_case_			,		tf.Tensor    ) and t.shape.rank == 1:
			return tf.expand_dims(snake_case_			,		axis=-1    )
		return t

	return tf.nest.map_structure(_expand_single_ad_tensor			,		snake_case_    )








"""simple docstring"""
from maths.prime_check import is_prime


def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if not isinstance(__snake_case ,     __snake_case  ):
              __magic_name__      				=    F"""Input value of [number={number}] must be an integer"""
              raise TypeError(__snake_case  )
       if is_prime(__snake_case  ) and is_prime(number + 2  ):
              return number + 2
       else:
              return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()